Category Archives: Artificial Intelligence
From targeted phishing campaigns to new stalking methods: there are plenty of ways that artificial intelligence could be used to cause harm if it fell into the wrong hands. A team of researchers decided to rank the potential criminal applications that AI will have in the next 15 years, starting with those we should worry the most about. At the top of the list of most serious threats? Deepfakes.
By using fake audio and video to impersonate another person, the technology can cause various types of harms, said the researchers. The threats range from discrediting public figures to influence public opinion, to extorting funds by impersonating someone's child or relatives over a video call.
The ranking was put together after scientists from University College London (UCL) compiled a list of 20 AI-enabled crimes based on academic papers, news and popular culture, and got a few dozen experts to discuss the severity of each threat during a two-day seminar.
SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)
The participants were asked to rank the list in order of concern, based on four criteria: the harm it could cause, the potential for criminal profit or gain, how easy the crime could be carried out and how difficult it would be to stop.
Although deepfakes might in principle sound less worrying than, say, killer robots, the technology is capable of causing a lot of harm very easily, and is hard to detect and stop. Relative to other AI-enabled tools, therefore, the experts established that deepfakes are the most serious threat out there.
There are already examples of fake content undermining democracy in some countries: in the US, for example, a doctored video of House Speaker Nancy Pelosi in which she appeared inebriated picked up more than 2.5 million views on Facebook last year.
UK organization Future Advocacy similarly used AI to create a fake video during the 2019 general election, which showed Boris Johnson and Jeremy Corbyn endorsing each other for prime minister. Although the video was not malicious, it stressed the potential of deepfakes to impact national politics.
The UCL researchers said that as deepfakes get more sophisticated and credible, they will only get harder to defeat. While some algorithms are already successfully identifying deepfakes online, there are many uncontrolled routes for modified material to spread. Eventually, warned the researchers, this will lead to widespread distrust of audio and visual content.
Five other applications of AI also made it to the "highly worrying" category. With autonomous cars just around the corner, driverless vehicles were identified as a realistic delivery mechanism for explosives, or even as weapons of terror in their own right. Equally achievable is the use of AI to author fake news: the technology already exists, stressed the report, and the societal impact of propaganda shouldn't be under-estimated.
Also keeping AI experts up at night are applications that will be so pervasive that defeating them will be near impossible. This is the case for AI-infused phishing attacks, for example, which will be perpetrated via crafty messages that will be impossible to distinguish from reality. Another example is large-scale blackmail, enabled by AI's potential to harvest large personal datasets and information from social media.
Finally, participants pointed to the multiplication of AI systems used for key applications like public safety or financial transactions and to the many opportunities for attack they represent. Disrupting such AI-controlled systems, for criminal or terror motives, could result in widespread power failures, breakdown of food logistics, and overall country-wide chaos.
UCL's researchers labelled some of the other crimes that could be perpetrated with the help of AI as only "moderately concerning". Among them feature the sale of fraudulent "snake-oil" AI for popular services like lie detection or security screening, or increasingly sophisticated learning-based cyberattacks, in which AI could easily probe the weaknesses of many systems.
Several of the crimes cited could arguably be seen as a reason for high concern. For example, the misuse of military robots, or the deliberate manipulation of databases to introduce bias, were both cited as only moderately worrying.
The researchers argued, however, that such applications seem too difficult to push at scale in current times, or could be easily managed, and therefore do not represent as imminent a danger.
SEE: AI, machine learning to dominate CXO agenda over next 5 years
At the bottom of the threat hierarchy, the researchers listed some "low-concern" applications the petty crime of AI, if you may. On top of fake reviews or fake art, the report also mentions burglar bots, small devices that could sneak into homes through letterboxes or cat flaps to relay information to a third party.
Burglar bots might sound creepy, but they could be easily defeated in fact, they could pretty much be stopped by a letterbox cage and they couldn't scale. As such, the researchers don't expect that they will cause huge trouble anytime soon. The real danger, according to the report, lies in criminal applications of AI that could be easily shared and repeated once they are developed.
UCL's Matthew Caldwell, first author of the report, said: "Unlike many traditional crimes, crimes in the digital realm can be easily shared, repeated, and even sold, allowing criminal techniques to be marketed and for crime to be provided as a service. This means criminals may be able to outsource the more challenging aspects of their AI-based crime."
The marketisation of AI-enabled crime, therefore, might be just around the corner. Caldwell and his team anticipate the advent of "Crime as a Service" (CaaS), which would work hand-in-hand with Denial of Service (DoS) attacks.
And some of these crimes will have deeper ramifications than others. Here is the complete ranking of AI-enabled crimes to look out for, as compiled by UCL's researchers:
3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success – Forbes
From the smallest local business to the largest global players, I believe every organization must embrace the AI revolution, and identify how AI (artificial intelligence) will make the biggest difference to their business.
3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success
But before you can develop a robust AI strategy in which you work out how best to use AI to drive business success you first need to understand whats possible with AI. To put it another way, how are other companies using AI to drive success?
Broadly speaking, organizations are using AI in three main ways:
Creating more intelligent products
Offering a more intelligent service
Improving internal business processes
Lets briefly look at each area in turn.
Creating more intelligent products
Thanks to the Internet of Things, a whole host of everyday products are getting smarter. What started with smartphones has now grown to include smart TVs, smartwatches, smart speakers, and smart home thermostats plus a range of more eyebrow-raising "smart" products such as smart nappies, smart yoga mats, smart office chairs, and smart toilets.
Generally, these smart products are designed to make customers lives easier and remove those annoying bugbears from everyday life. For example, you can now get digital insoles that slip into your running shoes and gather data (using pressure sensors) about your running style. An accompanying app will give you real-time analysis of your running performance and technique, thereby helping you avoid injuries and become a better runner.
Offering a more intelligent service
Instead of the traditional approach of selling a product or service as a one-off transaction, more and more businesses are transitioning to a servitization model, in which the product or service is delivered as an ongoing subscription. Netflix is a prime example of this model in action. For a less obvious example, how about the Dollar Shave Club, which will deliver razor blades and grooming products to your door on a regular basis. Or Stich Fix, a personalized styling service that delivers clothes to your door based on your personal style, size, and budget.
Intelligent services like this are reliant on data and AI. Businesses like Netflix have access to a wealth of valuable customer data data that helps the company provide a more thoughtful service, based on what it knows the customer really wants (whether its movies, clothes, grooming products or whatever).
Improving internal business processes
In theory, AI could be worked into pretty much any aspect of a business: manufacturing, HR, marketing, sales, supply chain and logistics, customer services, quality control, IT, finance and more.
From automated machinery and vehicles to customer service chatbots and algorithms that detect customer fraud, AI solutions and technologies are being incorporated into all sorts of business functions in order to maximize efficiency, save money and improve business performance.
So, which area should you focus on products, services, or business processes?
Every business is different, and how you decide to use AI may differ wildly from even your closest competitor. For AI to truly add value in your business, it must be aligned with your companys key strategic goals which means you need to be clear on what it is you're trying to achieve before you can identify how AI can help you get there.
That said, its well worth considering all three areas: products, services and business processes. Sure, one of the areas is likely to be more of a priority than the others, and that priority will depend on your companys strategic goals. But you shouldnt ignore the potential of the other AI uses.
For example, a product-based business might be tempted to skip over the potential for intelligent services, while a service-based company could easily think smart products arent relevant to its business model. Both might think AI-driven business processes are beyond their capabilities at this point in time.
But the most successful, most talked-about companies on the planet are those that deploy AI across all three areas. Take Apple as an example. Apple built its reputation on making and selling iconic products like the iPad. Yet, nowadays, Apple services (including Apple Music and Apple TV) generate more revenue than iPad sales. The company has transitioned from purely a product company to a service provider, with its iconic products supporting intelligent services. And you can be certain that Apple uses AI and data to enhance its internal processes.
In this way, AI can throw up surprising additions and improvements to your business model or even lead you to an entirely new business model that you never previously considered. It can lead you from products to services, or vice versa. And it can throw up exciting opportunities to enhance the way you operate.
Thats why I recommend looking at products, services, and business processes when working out your AI priorities. You may ultimately decide that optimizing your internal processes (for example, automating your manufacturing) is several years away, and thats fine. The important thing is to consider all the AI opportunities, so that you can properly prioritize what you want to achieve and develop an AI strategy that works for your business.
AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.
Experts Are Divided Over Future Of Artificial Intelligence But Agree On Its Growing Impact – Outlook India
As humans, we love contrast. It is no wonder that experts, while defining the impact of Artificial Intelligence (AI) on the future of humankind, are at two ends of the spectrum. One is a happy scenario of human beings and artificially intelligent machines coexisting in perfect harmony. Another is an Orwellian dystopia of AI dominance over human intelligence and civilization. While there may be disagreements about the future, everyone agrees on the impact and growing ubiquity of AI.
Let us look at the potential impact of AI on our society. Algorithms have been generally successful in predicting almost all the weather calamities (except, of course, earthquakes) with reasonable accuracies. Since we started using AI, global death share from natural disasters since 2010 has reduced from 0.47% of all world deaths to 0.02% in 2017. AI worked wonders in healthcare by increasing the accuracy and timeliness of disease detection. Using a combination of big data and machine learning algorithms, we can predict machine part failures better. Stability of electricity grids, metal productions and commodity prices are predicted with astonishing precisions.
Enterprises were quick to jump on the AI bandwagon. Big four tech companies are seen to have made most of it. In the midst of the pandemic, global news media on June 9 reported an all-time high share prices for these companieswith a combined market capitalisation of almost $5 trillion. These companies are changing the way we live, do business and relax. We navigate lot more smoothly now with maps on our phone and do not need a translator to understand another language. We have super-efficient digital assistants to manage our schedules intelligently and can buy essential items from our phones.
Consumer packed goods companies have started using big data and machine learning to determine which of the retail stores should get what commodity and at what price. Many of the manufacturing organisations worldwide have started using predictive analytics to analyse their planning efficiency. Using AI techniques, logistics and transportation companies have started planning significant route optimisation, reducing cost and delivering faster to ports. Banks, stock markets and insurance companies use data, machine learning techniques and natural language processing techniques to provide the precise stocks and other financial products recommendations to their customers. Transformative aspects of AI seem to be going beyond delivering powerful use cases and outcomes. It seems to be changing the model of business itself. Organisations are no longer getting measured by the number of employees, assets and real estate they hold. Classic adage of David killing a Goliath is not a fable anymore. AI seems to have the potential to take a powerful business opportunity, analyse a lot of data with powerful algorithms and present the outcome through multiple channels to bring transformation right at the doorsteps (or screens) of consumers. Possibly, that is where it is getting a bit worrisome.
In his 2018 best seller Factfulness, Dr. Hans Rosling points out five global risks that should worry the human race. He could not have been more prophetic. First of them was global pandemic; others being financial collapse, World War III, climate change and extreme poverty. In the middle of a significant disruption, AI is seen to present a real disturbing proposition. Can the enterprise bring in more automation to replace the severely depleted job markets? Can the potential of AI create a situation where powerful corporations and states with the power of algorithm, processing capability of big data get into a position of more unassailable lead - where they have absolute power and society? Would we be left with the intent and resources to focus on most important challenge in post COVID world - more people than ever in the state of extreme hunger?
German philosopher Arthur Schopenhauer wrote, Talent hits a target that no one else can hit, Genius hits the target no one else can see. Human geniuses have their limited time to shape the future as clock ticks on.
(The author is partner, Deloitte India. Views expressed are personal.)
COVID-19 Impacts: Artificial Intelligence-as-a-Service (AIaaS) Market Will Accelerate at a CAGR of Over 48% Through 2020-2024|Growing Adoption of…
LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.
Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Free Sample Report on COVID-19 Impact
Frequently Asked Questions-
The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.
The growing adoption of cloud-based solutions has been instrumental in driving the growth of the market.
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation
Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:
To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR41175
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope
Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our artificial intelligence-as-a-service (AIaaS) market report covers the following areas:
This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Vendor Analysis
We provide a detailed analysis of vendors operating in the artificial intelligence-as-a-service (AIaaS) market, including some of the vendors such as Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. Backed with competitive intelligence and benchmarking, our research reports on the artificial intelligence-as-a-service (AIaaS) market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.
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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights
Table of Contents:
Five Forces Analysis
Market Segmentation by End-user
Drivers, Challenges, and Trends
Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.
The sudden appearance and rapid spread of COVID-19 took governments and society by surprise. As they dusted off pandemic response plans and geared up to fight the virus, it became clear that we needed to turbo-charge R&D efforts and find better ways to hunt down promising treatments for emerging diseases.
Artificial intelligence (AI) has proven a powerful tool in this fight.
In a pandemic, speed is of the essence. Although scientists managed to sequence the genetic code of the new coronavirus and produce diagnostic tests in record time, developing drugs and vaccines against the virus remains a long haul.
AI has the power to accelerate the process by reasoning across all available biomedical data and information in a systematic search for existing approved medicines a vital step in helping patients while the world waits for a vaccine.
Machines excel in handling data in fast-changing circumstances, which means machine learning systems can be harnessed to work as tireless and unbiased super-researchers.
This is not just theory. In late January, using its proprietary platform of AI models and algorithms to search through the scientific literature, researchers at BenevolentAI in London identified an established, once-daily arthritis pill as a potential treatment for COVID-19. The findings were published in two papers in The Lancet and The Lancet Infectious Diseases, in line with our commitment under the Wellcome Trust pledge to share our coronavirus-related research rapidly and openly.
BenevolentAI's COVID-19 timeline
The discovery followed a computer-driven hunt for drug candidates with both antiviral and anti-inflammatory properties, since in severe cases of COVID-19 it is the bodys overactive immune response that can cause significant and sometimes fatal damage.
The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.
The BenevolentAI knowledge graph found that baricitinib might help treat COVID-19.
The system used to identify baricitinib was not actually set up to find new uses of existing medicines, but rather to discover and develop new drugs a sign of the potential for AI to uncover novel insights and relationships across an unlimited number of biological entities. In a crisis like COVID-19, it clearly makes sense to hunt through already approved drugs that can be ready for large-scale clinical trials until vaccines are approved and readily available in the global supply chain.
BenevolentAIs vision is to dramatically improve pharmaceutical R&D productivity across the board and to expand the drug discovery universe by making predictions in novel areas of biology. Currently, around half of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from mid-stage Phase 2 testing to approval.
Using a knowledge graph composed of chemical, biological and medical research and information, the companys AI machine learning models and algorithms can identify potential drug leads currently unknown in medical science and far faster than humans. While such systems will never replace scientists and clinicians, they can save both time and money. And the agnostic approach adopted by machine learning means such platforms can generate leads that may have been overlooked by traditional research.
The endeavour has already led to an in-house project on amyotrophic lateral sclerosis (ALS), ulcerative colitis, atopic dermatitis and programmes with partners on progressive kidney and lung diseases, as well as hard-to-treat cancers like glioblastoma.
The ability of machines to solve complex biological puzzles more rapidly than human experts has prompted increased investment in AI drug discovery by a growing number of large pharmaceutical companies.
And AI is also being harnessed in other areas of medicine, such as the analysis of medical images. This encompasses long-standing work on cancer scans and much more recent efforts to use computer power to identify COVID-19 from chest X-rays, including the open-access COVID-Net neural network.
The application of precision medicine to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseasesand disease in general.
But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.
The World Economic Forums Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access across borders to sensitive health data for the treatment and diagnosis of rare diseases.
The data will be shared via a federated data system: a decentralized approach that allows different institutions to access each others data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.
The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).
Clearly, COVID-19 has been a wake-up call for the world. It seems this outbreak may be part of an increasingly frequent pattern of epidemics, fuelled by our hyper-connected modern world. As a result, medical experts are braced for more previously unknown Disease X threats in the years ahead as viruses jump from animals to humans and jet around the world.
Technology has helped create a world in which pathogens like COVID-19, SARS and Zika can spread. But technology, in the form of AI, can also provide us with the weapons to fight back.
Job interviews: Recruiters are using artificial intelligence to analyse what you say to find the right hire – TechRepublic
Harqen's AI platform analyses language to determine a candidate's suitability for a role, potentially making it less prone to bias than video-based recruitment technology.
Artificial-intelligence-based hiring tools are already transforming the recruitment process by allowing businesses to vastly speed up the time it takes to identify top talent. With algorithms able to scour applications databases in the fraction of a time it would take a human hiring manager, AI-assisted hiring has the potential not only to give precious time back to businesses, but also draw in candidates from wider and more diverse talent pools.
AI-assisted hiring is also posited as a potential solution for reducing human bias whether subconscious or otherwise in the hiring process.
SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)
US company Harqen has been offering hiring technologies to some of the world's biggest companies for years, partnering with the likes of Walmart, FedEx and American Airlines to streamline and improve their hiring processes. Originating as an on-demand interviewing provider, the company has now expanded into AI with a new platform that it says offers a more dependable and bias-free means of matching employers with employees.
The solution, simply called the Harqen Machine Learning Platform, analyses candidate's answers to interview questions and assesses the type of words and language used in their responses. According to Harqen, this allows it to put together a profile of psychological traits that can be used to help determine a candidate's suitability for a role.
Combined with a resume analysis, which provides a more straightforward determiner of whether a candidate's professional and educational background fits with the requirements of the job, Harqen says its machine-learning platform is capable of making the same hiring decision as human recruiters 95% of the time. In one campaign that assessed approximately 3,500 job applications with "a very large US diagnostic firm" in early 2020, Harqen's machine-learning platform successfully predicted 2,193 of the candidate applications that were accepted, and 1,292 that were declined.
Key to Harqen's offering is what the company's chief technology officer Mark Unak describes as the platform's linguistic analysis, which can identify word clusters that are specific to certain job types but also offers a personality analysis based on the so-called "big five" traits, also known as the OCEAN model (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism), which can help hiring managers determine a candidate's enthusiasm for the position.
"We have a dictionary of terms where most positive words are ranked as a +5 and most negative words are ranked as a -5, so we can determine how enthusiastic you are in the answers that you're giving," Unak tells TechRepublic.
"We can also use a linguistic analysis to analyse the grammar," he adds, noting that about 60% of our vocabulary consists of just 80 words. Those are the pronouns, the propositions, the articles and the intransigent verbs. "The remaining 10,000 words in the English language fill in that 40%. By the analysis of how you use that, we can get a psychological trait analysis."
Harqen's machine-learning tool analyses word clusters to help determine candidates' personality traits, such as enthusiasm.
According to Unak, using a machine-learning system that determines a candidate's suitability based on linguistic analysis is a more accurate and impartial method than those that rely on facial-scanning or vocal-inflection algorithms. Such machine-learning techniques within hiring are on the rise and are increasingly being adopted by major companies around the world.
"That's kind of problematic," says Unak. "Not everybody expresses emotions in the same way, with the same facial expressions, and not everybody expresses the same emotion that's expected. Different cultures and different races might have different problems in expressing those facial expressions and having the computer recognise them."
SEE:Diversity and Inclusion policy (TechRepublic Premium)
By only analysing the linguistic content that has been transcribed from recorded interviews, Harqen's algorithm never factors in appearance, facial expressions, or other self-reported personality traits that could be unreliable. Unak says the company will also retrain its models on a regular basis as new data comes in, which will help ensure that algorithms don't get stuck in their old ways if candidates begin giving new answers to questions that are equally relevant.
"If our customer evolves and they start to hire people who are either more diverse, or come up with different answers to the questions that are just as relevant, our models will pick that up," Unak adds.
Diversity whether based on gender, race, age or otherwise has been show to play a significant role in the success or failure of workplace productivity and collaboration. Whether AI-based hiring tools can help here remains to be seen, and ultimately depends on whether they can be implemented in a fair and impartial way.
Beyond diversity, Harqen is exploring how its machine-learning tool could help businesses get the best return on investment form their hiring choices. The magic word here is delayed gratification: the ability to accurately identify employees who can resist the temptation for immediate rewards and instead persevere for an even greater payoff in the future.
"It's grit, it's persistence, it's the ability to imagine a future and it's the ability to develop and execute a plan to get there," says Unak. "Isn't that what hope and delayed gratification mean? I hope for a better future, I can imagine it, my hope is realistic and that there's a plan or a way to get there, and I'm going to work towards it."
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While Artificial Intelligence (AI) has been prevalent in industries such as the financial sector, where algorithms and decision trees have long been used in approving or denying loan requests and insurance claims, the manufacturing industry is at the beginning of its AI journey. Manufacturers have started to recognize the benefits of embedding AI into business operationsmarrying the latest techniques with existing, widely used automation systems to enhance productivity.
A recent international IFS study polling 600 respondents, working with technology including Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), and Field Service Management (FSM), found more than 90 percent of manufacturers are planning AI investments. Combined with other technologies such as 5G and the Internet of Things (IoT), AI will allow manufacturers to create new production rhythms and methodologies. Real-time communication between enterprise systems and automated equipment will enable companies to automate more challenging business models than ever before, including engineer-to-order or even custom manufacturing.
Despite the productivity, cost-savings and revenue gains, the industry is now seeing the first raft of ethical questions come to the fore. Here are the three main ethical considerations companies must weigh-up when making AI investments.
At first, AI in manufacturing may conjure up visions of fully automated smart factories and warehouses, but the recent pandemic highlighted how AI can play a strategic role in the back-office, mapping different operational scenarios and aiding recovery planning from a finance standpoint. Scenario planning will become increasingly important. This is relevant as governments around the world start lifting lockdown restrictions and businesses plan back to work strategies. Those simulations require a lot of data but will be driven by optimization, data analysis and AI.
And of course, it is still relevant to use AI/Machine Learning to forecast cash. Cash is king in business right now. So, there will be an emphasis on working out cashflows, bringing in predictive techniques and scenario planning. Businesses will start to prepare ways to know cashflow with more certainty should the next pandemic or crisis occur.
For example, earlier in the year the conversation centered on the just-in-time scenarios, but now the focus is firmly on what-if planning at the macro supply chain level:
Another example is how you can use a Machine Learning service and internal knowledge base to facilitate Intelligent Process Automation allowing recommendations and predictions to be incorporated into business workflows, as well as AI-driven feedback on how business processes themselves can be improved or automated.
The closure of manufacturing organizations and reduction in operations due to depleting workforces highlight AI technology in the front-office isnt perhaps as readily available as desired, and that progress needs to be made before it can truly provide a level of operational support similar to humans.
Optimists suggest AI may replace some types of labor, with efficiency gains outweighing transition costs. They believe the technology will come to market at first as a guide-on-the-side for human workers, helping them make better decisions and enhancing their productivity, while having the potential to upskill existing employees and increase employment in business functions or industries that are not in direct competition with AI.
Indeed, recent IFS research points to an encouraging future for a harmonized AI and human workforce in manufacturing. The IFS AI study revealed that respondents saw AI as a route to create, rather than cull, jobs. Around 45 percent of respondents stated they expect AI to increase headcount, while 24 percent believe it wont impact workforce figures.
The pandemic has demonstrated AI hasnt developed enough to help manufacturers maintain digital-only operations during unforeseen circumstances, and decision makers will be hoping it can play a greater role to mitigate extreme situations in the future.
It is easy for organizations to say they are digitally transforming. They have bought into the buzzwords, read the research, consulted the analysts, and seen the figures about the potential cost savings and revenue growth.
But digital transformation is no small change. It is a complete shift in how you select, implement and leverage technology, and it occurs company-wide. A critical first step to successful digital transformation is to ensure that you have the appropriate stakeholders involved from the very beginning. This means manufacturing executives must be transparent when assessing and communicating the productivity and profitability gains of AI against the cost of transformative business changes to significantly increase margin.
When businesses first invested in IT, they had to invent new metrics that were tied to benefits like faster process completion or inventory turns and higher order completion rates. But manufacturing is a complex territory. A combination of entrenched processes, stretched supply chains, depreciating assets and growing global pressures makes planning for improved outcomes alongside day-to-day requirements a challenging prospect. Executives and their software vendors must go through a rigorous and careful process to identify earned value opportunities.
Implementing new business strategies will require capital spending and investments in process change, which will need to be sold to stakeholders. As such, executives must avoid the temptation of overpromising. They must distinguish between the incremental results they can expect from implementing AI in a narrow or defined process as opposed to a systemic approach across their organization.
There can be intended or unintended consequences of AI-based outcomes, but organizations and decision makers must understand they will be held responsible for both. We have to look no further than tragedies from self-driving car accidents and the subsequent struggles that followed as liability is assigned not on the basis of the algorithm or the inputs to AI, but ultimately the underlying motivations and decisions made by humans.
Executives therefore cannot afford to underestimate the liability risks AI presents. This applies in terms of whether the algorithm aligns with or accounts for the true outcomes of the organization, and the impact on its employees, vendors, customers and society as a whole. This is all while preventing manipulation of the algorithm or data feeding into AI that would impact decisions in ways that are unethical, either intentionally or unintentionally.
Margot Kaminski, associate professor at the University of Colorado Law School, raised the issue of automation biasthe notion that humans trust decisions made by machines more than decisions made by other humans. She argues the problem with this mindset is that when people use AI to facilitate decisions or make decisions, they are relying on a tool constructed by other humans, but often they do not have the technical capacity, or practical capacity, to determine if they should be relying on those tools in the first place.
This is where explainable AI will be criticalAI which creates an audit path so both before and after the fact, there is a clear representation of the outcomes the algorithm is designed to achieve and the nature of the data sources it is working form. Kaminski asserts explainable AI decisions must be rigorously documented to satisfy different stakeholdersfrom attorneys to data scientists through to middle managers.
Manufacturers will soon move past the point of trying to duplicate human intelligence using machines, and towards a world where machines behave in ways that the human mind is just not capable. While this will reduce production costs and increase the value organizations are able to return, this shift will also change the way people contribute to the industry, the role of labor, and civil liability law.
There will be ethical challenges to overcome, but those organizations who strike the right balance between embracing AI and being realistic about its potential benefits alongside keeping workers happy will usurp and take over. Will you be one of them?
Originally posted here:
3 Ethical Considerations When Investing in AI - Manufacturing Business Technology
The COVID 19 situation, has rendered the industry into an unprecedented situation. Businesses across the globe are now resorting to plan out new strategies to keep the operations going, to meet clients demands.
Work-from-Home is the new normal for both the employees and the employers to function in a mitigated manner. Twitter on their tweet had suggested their employees, to function through Work-from-Home, forever, if they want to. This new trend can be easily surmised as being effective for a while to manage operations, but cannot be ruled out as the necessary solution, for satisfying the customers and clients in the long run.
Companies need to employ ethically approved ideas and strategies that would assure employees, clients, and customers, without breaching the data.
With the present situation, where social distancing is a must, classroom training cannot be ruled out as the plausible solution for training employees. Thats where Virtual Reality comes into play.
Virtual Reality (VR), which was earlier ruled out to be used in the gaming interface has now the potential to become the face of the industrial enterprise. Areportby PwC states that VR and Augmented Reality has the potential to surge US$1.5trillion globally by the year 2030. Another report by PwC states that VR can train employees four times faster than classroom training. Individuals trained through VR has confidence 2.5 times more than those who are trained through classroom programs or e-courses, and 2.3 times more emotionally inclined towards the content that they are working on. Employees trained using VR are also 1.5 times more focused than that through classroom programs and e-courses.
The only drawback in using PwC will be in its cost-effectiveness as it is 47 percent costlier than classroom courses.
Ever since its evolution, one of the major concerns regarding AI amongst clients, customers, and employees is the breach of ethical AI practices. A report byCapgemini Research Institutestates that amongst 62% of customers who were surveyed would like to place their trust in an organization that practices AI ethically.
For any organization to keep its business and employees safe during the time of crisis, the development of an ethically viable AI is a must. This can only be achieved by practicing ethical use of AI applications, informing and educating the customers about the practices of AI.
Areportby PwC, states that planning out a new strategy in both data and technology, evaluating the ethical flaws associated with the existing data, and only collecting the required amount of data, would help in maintaining trust amongst both the customers and employees.
Given the present situation, sales executives are facing a daunting task of maintaining their operations. However, the use of AI can easily redeem this time consuming and laborious task. Withthe use of an AI algorithm, the sales executive or manager can identify the higher probable inclination of the client towards a particular service. The AI algorithm would also, help in offering a new product according to the pre-requisite preferences of the client.
In the time of crisis, new solutions must be thought about for repurposing business. PwC states that this can be achieved by repurposing business assets, forming a new business partnership, rapid innovation, and testing and learning.
This will not only help in building trust amongst employees but also build resilience within the organization, for the future endeavor.
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Artificial Intelligence in Business: The New Normal in Testing Times - Analytics Insight
RadNet and Hologic Announce Collaboration to Advance the Development of Artificial Intelligence Tools in Breast Health – GlobeNewswire
LOS ANGELES and MARLBOROUGH, Mass., Aug. 06, 2020 (GLOBE NEWSWIRE) -- RadNet, Inc. (Nasdaq: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services, and Hologic, Inc. (Nasdaq: HOLX), an innovative medical technology company primarily focused on improving womens health, have entered into a definitive collaboration to advance the use of artificial intelligence (A.I.) in breast health.
As the world leader in mammography, Hologic will contribute capabilities and insights behind its market-leading hardware and software, and will benefit from access to data produced by RadNets fleet of high-resolution mammography systems, the largest in the nation, to train and refine current and future products based on A.I. RadNet will share data from its extensive network of imaging centers, as well as provide in-depth knowledge of the patient pathway and workflow needs to help make a positive impact across the breast care continuum. The collaboration will enable new joint market opportunities and further efforts to build clinician confidence and develop and integrate new A.I. technologies.
We believe the future of breast health will rely heavily on the integration of A.I. tools, such as our 3DQuorum imaging technology, as well as next generation CAD software, that aid in the early detection of breast cancer, said PeteValenti, Hologics Division President, Breast and Skeletal Health Solutions. We are energized by the opportunities this transformative collaboration with RadNet creates for patients and clinicians alike. Access to data is critical in training and refining A.I. algorithms. With this collaboration, we now have the opportunity to leverage data from the largest fleet of high-resolution mammography systems to develop new tools across the continuum of care, provide workflow efficiencies, and improve patient satisfaction and outcomes.
As part of its collaboration with Hologic, RadNet intends to upgrade its entire fleet of Hologic mammography systems to feature Hologics 3DQuorum imaging technology, powered by Genius AI. This technology works in tandem with Clarity HD high resolution imaging technology to reduce tomosynthesis image volume for radiologists by 66 percent.i Additionally, all of RadNets Hologic systems are anticipated to feature the Genius 3D Mammography exam, the only mammogram clinically proven and FDA approved as superior for all women, including those with dense breasts, compared with 2D mammography alone. ii,iii,iv,v
The collaboration will be bolstered by RadNets recent acquisition of DeepHealth (Cambridge, MA), which uses machine learning to develop software tools to improve cancer detection and provide clinical decision support. Led by Dr. Gregory Sorensen, DeepHealths team of A.I. experts is focused on enabling industry-leading care by providing products that clinicians and patients can trust. In addition, the DeepHealth team will integrate its A.I. tools within the Hologic ecosystem. When seeking a partner and reviewing options amongst all mammography vendors, we selected to integrate our tools with Hologics market-leading technology, said Dr. Sorensen. Hologics systems produce the highest level of spatial resolution in the market. Hologic also has the largest domestic footprint and market share in 3D Mammography systems. This integration will allow the DeepHealth team to train its algorithms for use with the most advanced screening technology possible. As Hologic and RadNet share their respective capabilities and tools, greater efficiency and accuracy can be achieved by our radiologists.
Much like RadNet, Hologic is a highly innovative company and market leader in breast health, said Howard Berger, MD, RadNets Chairman and CEO. When Hologics leading screening technology is paired with RadNets approximately 1.2 million annual screening mammograms, the resulting dataset becomes a powerful tool to train algorithms. We see the future as being transformative for both of our organizations.
We have witnessed how the application of our Genius AI technology platform has improved cancer detection, operational efficiency and clinical decision support across the breast cancer care continuum, said Samir Parikh, Hologics Global Vice President for Research and Development, Breast and Skeletal Health Solutions. We look forward to building upon these advances in collaboration with Dr. Sorensen and the RadNet team to expand the use of machine learning, big data applications and automated algorithms impacting global breast care.
About RadNet, Inc.RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 335 owned and/or operated outpatient imaging centers. RadNet's core markets include California, Maryland, Delaware, New Jersey and New York. In addition, RadNet provides radiology information technology solutions, teleradiology professional services and other related products and services to customers in the diagnostic imaging industry. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technicians, RadNet has a total of approximately 8,600 employees. For more information, visit http://www.radnet.com.
About Hologic, Inc.Hologic, Inc. isan innovative medical technology company primarily focused on improving womens health and well-being through early detection and treatment.For more information on Hologic, visitwww.hologic.com.
The Genius 3D Mammography exam (also known as the Genius exam) is only available on a Hologic 3D Mammography system. It consists of a 2D and 3D image set, where the 2D image can be either an acquired 2D image or a 2D image generated from the 3D image set. There are more than 6,000 Hologic 3D Mammography systems in use in the United States alone, so women have convenient access to the Genius exam. To learn more, visit http://www.Genius3DNearMe.com.
Hologic, 3D Mammography, 3DQuorum, 3Dimensions, Clarity HD, Genius and Genius AI are trademarks and/or registered trademarks of Hologic, Inc., and/or its subsidiaries in the United States and/or other countries.
Forward-Looking StatementsThis news release may contain forward-looking information that involves risks and uncertainties, including statements about the use of Hologic products. There can be no assurance these products will achieve the benefits described herein or that such benefits will be replicated in any particular manner with respect to an individual patient, as the actual effect of the use of the products can only be determined on a case-by-case basis. In addition, there can be no assurance that these products will be commercially successful or achieve any expected level of sales. Hologic and RadNet expressly disclaim any obligation or undertaking to release publicly any updates or revisions to any such statements presented herein to reflect any change in expectations or any change in events, conditions or circumstances on which any such data or statements are based.
This information is not intended as a product solicitation or promotion where such activities are prohibited. For specific information on what products are available for sale in a particular country, please contact a local Hologic sales representative or write to firstname.lastname@example.org.
Media and Investor Contact RadNet, Inc.:Mark StolperExecutive Vice President & Chief Financial Officer310-445-2800
Media Contact Hologic, Inc.:Jane Mazur508-263-8764 (direct)585-355-5978 (mobile)
Investor Contact Hologic, Inc.:Michael Watts858-410-8588
i Report: CSR-00116
ii Results from Friedewald, SM, et al. "Breast cancer screening using tomosynthesis in combination with digital mammography." JAMA 311.24 (2014): 2499-2507; a multi-site (13), non-randomized, historical control study of 454,000 screening mammograms investigating the initial impact the introduction of the Hologic Selenia Dimensions on screening outcomes. Individual results may vary. The study found an average 41% increase and that 1.2 (95% CI: 0.8-1.6) additional invasive breast cancers per 1000 screening exams were found in women receiving combined 2D FFDM and 3D mammograms acquired with the Hologic 3D Mammography System versus women receiving 2D FFDM mammograms only.
iii Freidewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, Hayes MK, Copit DS, Carlson KL, Cink TM, Carke LD, Greer LN, Miller DP, Conant EF, Breast Cancer Screening Using Tomosynthesis in Combination with Digital Mammography,JAMAJune 25, 2014.
iv Bernardi D, Macaskill P, Pellegrini M, etal. Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study.Lancet Oncol.2016 Aug;17(8):1105-13.
v FDA submissions P080003, P080003/S001, P080003/S004, P080003/S005
The COVID-19 pandemic has had a profound impact across industries and healthcare in particularevery aspect of it is undergoing changefrom diagnosis to treatment and through the entire continuum of care. This has also created an urgency in the healthcare industry, to look for innovative solutions and a boost to the faster, efficient application of technologies like Artificial Intelligence (AI) and Deep Learning. Pathology is one area which stands to greatly benefit from these applications.
Pathologists today spend a significant amount of time observing tissue samples under a microscope and they are facing resource shortages, growing complexity of requests, and workflow inefficiencies with the growing burden of diseases. Their work underpins every aspect of patient care, from diagnostic testing and treatment advice to the use of cutting-edge genetic technologies. They also have to work together in a multidisciplinary team of doctors, scientists and healthcare professionals to diagnose, treat and prevent illness. With increasing emphasis on sub-specialisation, taking a second opinion from specialists, means shipping several glass slides across laboratories, sometimes to another country. This means reduced efficiency and delayed diagnosis and treatment. The current situation has disrupted this workflow.
Digitization in pathology
Digitization in Pathology has enabled an increase in efficiency, speed and enhanced quality of diagnosis. Recent technological advances have accelerated the adoption of digitisation in pathology, similar to the digital transformation that radiology departments have experienced over the last decade. Digital Pathology has enabled the conversion of the traditional glass slide to a digital image, which can then be viewed on a monitor, annotated, archived and shared digitally across the globe, for consultation based on organ sub-specialisation. With digitisation, a vast data set has become available, supporting new insights to pathologists, researchers, and pharmaceutical development teams.
The promise of AI
The availability of vast data is enabling the use of Artificial Intelligence methods, to further transform the diagnosis and treatment of diseases at an unprecedented pace. Human intelligence assisted with articial intelligence can provide a well-balanced view of what neither of them could do on their own. The evolution of Deep Learning neural networks and the improvement in accuracy for image pattern recognition has been staggering in the last few years. Similar to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time improving it a little to achieve more accurate outcomes.
The approach to diagnosis that incorporates multiple sources of data (e.g., pathology, radiology, clinical, molecular and lab operations) and using mathematical models to generate diagnostic inferences and presenting with clinically actionable knowledge to customers is Computational Pathology. Computational Pathology systems are able to correlate patterns across multiple inputs from the medical record, including genomics, enhancing a pathologists diagnostic capabilities, to make a more precise diagnosis. This allows Pathologists to eliminate tedious and time-consuming tasks while focusing more on interpreting data and detailing the implications for a patients diagnosis.
AI applications that can easily augment a Pathologists cognitive ability and save time are, for example, identifying the sections of greatest interest in biopsies, finding metastases in the lymph nodes of breast cancer patients, counting mitoses for cancer grading or measuring tumors point-to-point. The ultimate goal going forward is the integration of all these tools and algorithms into the existing workflow and make it seamless and more efficient.
However, Artificial Intelligence in Pathology is quite complex. The IT infrastructure required in terms of data storage, network bandwidth and computing power is significantly higher as compared to Radiology. Digitisation of Whole Slide Images (WSI) in pathology generate large amounts of gigapixel sized images and processing them needs high-performance computing. Training a deep learning network on a whole slide image at full resolution can be very challenging. With the increase in the processing power with the use of GPUs, there is a promise to train deep learning networks successfully, starting with training smaller regions of interest.
Another key aspect for training deep learning algorithms is the need for large amounts of labeled data. For supervised learning, a ground truth must first be included in the dataset to provide appropriate diagnostic context and this will be time-consuming. Obtaining adequately labeled data by experts is the key.
Digitisation in pathology supported by appropriate IT infrastructure is enabling Pathologists to work remotely without the need to wait for glass slides to be delivered and maintaining social distancing norms. The promise of Artificial Intelligence will only further accelerate the seamless integration of algorithms into the existing workflow. These unprecedented times have raised many challenges, but are also providing us a chance to accelerate the application of AI and in turn to achieve the quadruple aim: enhancing the patient experience, improving health outcomes, lowering the cost of care, and improving the work-life of care providers.